Abstract: In order to improve the accuracy of load forecasting results, a short-term load forecasting model based on improved fractal theory is designed. The date similar to the meteorological data is selected as the reference date, and the reference date is analyzed by re-scale-range method to determine that the reference date had fractal characteristics. The Iterative compression factor is calculated according to the fractal interpolation interval and the IFS (Iterative Function System) is established to establish the fractal interpolation Function of the reference date. The moving average function is used to process the data, and OLS (Ordinary Least Square) is used to establish the data fitting equation, and the time data is put into the fitting equation to calculate the predicted data. After simulation comparison experiment, the MAPE ( Mean Absolute Percentage Error ) of load data predicted by the improved prediction model decreases by 0. 26 compared with the previous prediction model. It is proved that the short-term power load forecasting model based on improved fractal theory can effectively improve the accuracy of load forecasting results. 

Key words: fractal interpolation function ( FIF)')">

fractal interpolation function ( FIF), iterative function system ( IFS), power load forecasting, nonlinear theory, rescale range analysis